Correspondence Between “Stable” Flame Macrostructure and Thermo-acoustic Instability in Premixed Swirl-Stabilized Turbulent Combustion

In this paper, we conduct an experimental investigation to study the link between the flame macroscale structure—or flame brush spatial distribution—and thermo-acoustic instabilities, in a premixed swirl-stabilized dump combustor. We operate the combustor with premixed methane–air in the range of equivalence ratio (φ) from the lean blowout limit to φ=0.75. First, we observe the different dynamic modes in this lean range as φ is raised. We also document the effect of φ on the flame macrostructure. Next, we examine the correspondence between dynamic mode transitions and changes in flame macrostructure. To do so, we modify the combustor length—by downstream truncation—without changing the underlying flow upstream. Thus, the resonant frequencies of the geometry are altered allowing for decoupling the heat release rate fluctuations and the acoustic feedback. Mean flame configurations in the modified combustor and for the same range of equivalence ratio are examined, following the same experimental protocol. It is found that not only the same sequence of flame macrostructures is observed in both combustors but also that the transitions occur at a similar set of equivalence ratio. In particular, the appearance of the flame in the outside recirculation zone (ORZ) in the long combustor—which occurs simultaneously with the onset of instability at the fundamental frequency—happens at similar φ when compared to the short combustor, but without being in latter case accompanied by a transition to thermo-acoustic instability. Then, we interrogate the flow field by analyzing the streamlines, mean, and rms velocities for the nonreacting flow and the different flame types. Finally, we focus on the transition of the flame to the ORZ in the acoustically decoupled case. Our analysis of this transition shows that it occurs gradually with an intermittent appearance of a flame in the ORZ and an increasing probability with φ. The spectral analysis of this phenomenon—we refer to as “ORZ flame flickering”—shows the presence of unsteady events occurring at two distinct low frequency ranges. A broad band at very low frequency in the range ∼(1 Hz–10 Hz) associated with the expansion and contraction of the inner recirculation zone (IRZ) and a narrow band centered around 28 Hz which is the frequency of rotation of the flame as it is advected by the ORZ flow.King Fahd University of Petroleum and Minerals (Grant R12-CE-10)King Abdullah University of Science and Technology (Grant KUS-110-010-01

A New Fractional-Order Load Frequency Control for Multi-Renewable Energy Interconnected Plants Using Skill Optimization Algorithm

Connection between electric power networks is essential to cover any deficit in the generation of power from any of them. The exchange powers of the plants during load disturbance should not be violated beyond their specified values. This can be achieved by installing load frequency control (LFC); therefore, this paper proposes a new metaheuristic-based approach using a skill optimization algorithm (SOA) to design a fractional-order proportional integral derivative (FOPID)-LFC approach with multi-interconnected systems. The target is minimizing the integral time absolute error (ITAE) of frequency and exchange power violations. Two power systems are investigated. The first one has two connected plants of photovoltaic (PV) and thermal units. The second system contains four plants, namely, PV, wind turbine, and two thermal plants, with governor dead-band (GDB) and generation rate constraints (GRC). Different load disturbances are analyzed in both considered systems. Extensive comparisons to the use of chef-based optimization algorithm (CBOA), jumping spider optimization algorithm (JSOA), Bonobo optimization (BO), Tasmanian devil optimization (TDO), and Atomic orbital search (AOS) are conducted. Moreover, statistical tests of Friedman ANOVA table, Wilcoxon rank test, Friedman rank test, and Kruskal Wallis test are implemented. Regarding the two interconnected areas, the proposed SOA achieved the minimum fitness value of 1.8779 pu during 10% disturbance on thermal plant. In addition, it outperformed all other approaches in the case of 1% disturbance on the first area as it achieved ITAE of 0.0327 pu. The obtained results proved the competence and reliability of the proposed SOA in designing an efficient FOPID-LFC in multi-interconnected power systems with multiple sources

Robust Parameter Identification Strategy for Lead Acid Battery Model

The most popular approach for smoothing renewable power generation fluctuations is to use a battery energy storage system. The lead-acid battery is one of the most used types, due to several advantages, such as its low cost. However, the precision of the model parameters is crucial to a reliable and accurate model. Therefore, determining actual battery storage model parameters is required. This paper proposes an optimal identification strategy for extracting the parameters of a lead-acid battery. The proposed identification strategy-based metaheuristic optimization algorithm is applied to a Shepherd model. The bald eagle search algorithm (BES) based identification strategy provided excellent performance in extracting the battery’s unknown parameters. As a result, the proposed identification strategy’s total voltage error has been reduced to 2.182 × 10−3, where the root mean square error (RMSE) between the model and the data is 6.26 × 10−5. In addition, the optimization efficiency achieved 85.32% using the BES algorithm, which approved its efficiency

A Comparison of Different Renewable-Based DC Microgrid Energy Management Strategies for Commercial Buildings Applications

DC microgrid systems allow commercial buildings to use locally generated energy and achieve an optimal economy efficiently. Economical and eco-friendly energy can be achieved by employing renewable energy sources. However, additional controllable sources, such as fuel cells, are required because of their reduced efficiency and fluctuated nature. This microgrid can use energy storage systems to supply transient power and enhance stability. The functioning of the microgrid and its efficiency are related to the implemented energy management strategy. In this paper, a comparison of several reported energy management strategies is fulfilled. The considered EMSs include the fuzzy logic control (FLC) strategy, the state machine control (SMC) strategy, the equivalent consumption minimization strategy (ECMS), and external energy maximization strategy (EEMS). These strategies are compared in terms of power-saving, system efficiency, and power quality specifications. The overall results confirm the ability of EEMS (high efficiency of 84.91% and economic power-saving 6.11%) and SMC (efficiency of 84.18% with high power-saving 5.07%) for stationary applications, such as building commercial applications. These strategies provide other advantages, which are discussed in detail in this paper

A New Fractional-Order Load Frequency Control for Multi-Renewable Energy Interconnected Plants Using Skill Optimization Algorithm

Connection between electric power networks is essential to cover any deficit in the generation of power from any of them. The exchange powers of the plants during load disturbance should not be violated beyond their specified values. This can be achieved by installing load frequency control (LFC); therefore, this paper proposes a new metaheuristic-based approach using a skill optimization algorithm (SOA) to design a fractional-order proportional integral derivative (FOPID)-LFC approach with multi-interconnected systems. The target is minimizing the integral time absolute error (ITAE) of frequency and exchange power violations. Two power systems are investigated. The first one has two connected plants of photovoltaic (PV) and thermal units. The second system contains four plants, namely, PV, wind turbine, and two thermal plants, with governor dead-band (GDB) and generation rate constraints (GRC). Different load disturbances are analyzed in both considered systems. Extensive comparisons to the use of chef-based optimization algorithm (CBOA), jumping spider optimization algorithm (JSOA), Bonobo optimization (BO), Tasmanian devil optimization (TDO), and Atomic orbital search (AOS) are conducted. Moreover, statistical tests of Friedman ANOVA table, Wilcoxon rank test, Friedman rank test, and Kruskal Wallis test are implemented. Regarding the two interconnected areas, the proposed SOA achieved the minimum fitness value of 1.8779 pu during 10% disturbance on thermal plant. In addition, it outperformed all other approaches in the case of 1% disturbance on the first area as it achieved ITAE of 0.0327 pu. The obtained results proved the competence and reliability of the proposed SOA in designing an efficient FOPID-LFC in multi-interconnected power systems with multiple sources

Parcel delivery by vehicle and drone

We investigate a single-vehicle parcel delivery problem in which customers may be served either by the vehicle or by a portable companion drone launched from the vehicle. The problem may be viewed as a Traveling Salesman Problem with Drone (TSP-D), and is modelled as a 0-1 mixed-integer program (MIP) that synchronizes vehicle and drone operations with the objective of minimizing the duration of the joint tour. Using a combination of valid inequalities, pre-processing, and other bound tightening strategies, we enhance the tractability of the proposed MIP formulation. - 2019, - 2019 Operational Research Society.Scopu

Self-adaptive Equilibrium Optimizer for solving global, combinatorial, engineering, and Multi-Objective problems

This paper proposes a self-adaptive Equilibrium Optimizer (self-EO) to perform better global, combinatorial, engineering, and multi-objective optimization problems. The new self-EO algorithm integrates four effective exploring phases, which address the potential shortcomings of the original EO. We validate the performances of the proposed algorithm over a large spectrum of optimization problems, i.e., ten functions of the CEC'20 benchmark, three engineering optimization problems, two combinatorial optimization problems, and three multi-objective problems. We compare the self-EO results to those obtained with nine other metaheuristic algorithms (MAs), including the original EO. We employ different metrics to analyze the results thoroughly. The self-EO analyses suggest that the self-EO algorithm has a greater ability to locate the optimal region, a better trade-off between exploring and exploiting mechanisms, and a faster convergence rate to (near)-optimal solutions than other algorithms. Indeed, the self-EO algorithm reaches better results than the other algorithms for most of the tested functions

Optimal Energy Management for Hydrogen Economy in a Hybrid Electric Vehicle

Fuel cell hybrid electric vehicles (FCEVs) are mainly electrified by the fuel cell (FC) system. As a supplementary power source, a battery or supercapacitor (SC) is employed (besides the FC) to enhance the power response due to the slow dynamics of the FC. Indeed, the performance of the hybrid power system mainly depends on the required power distribution manner among the sources, which is managed by the energy management strategy (EMS). This paper considers an FCEV based on the proton exchange membrane FC (PEMFC)/battery/SC. The energy management strategy is designed to ensure optimum power distribution between the sources considering hydrogen consumption. Its main objective is to meet the electric motor’s required power with economic hydrogen consumption and better electrical efficiency. The proposed EMS combines the external energy maximization strategy (EEMS) and the bald eagle search algorithm (BES). Simulation tests for the Extra-Urban Driving Cycle (EUDC) and New European Driving Cycle (NEDC) profiles were performed. The test is supposed to be performed in typical conditions t = 25 °C on a flat road without no wind effect. In addition, this strategy was compared with the state machine control strategy, classic PI, and equivalent consumption minimization strategy. In terms of optimization, the proposed approach was compared with the original EEMS, particle swarm optimization (PSO)-based EEMS, and equilibrium optimizer (EO)-based EEMS. The results confirm the ability of the proposed strategy to reduce fuel consumption and enhance system efficiency. This strategy provides 26.36% for NEDC and 11.35% for EUDC fuel-saving and efficiency enhancement by 6.74% for NEDC and 36.19% for EUDC